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bart-large-cnn-finetuned-scope-summarization

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. runs/Feb29_12-05-47_nxrm5cf9f5/events.out.tfevents.1709208438.nxrm5cf9f5.212.0 +2 -2
  4. runs/Feb29_12-05-47_nxrm5cf9f5/events.out.tfevents.1709210890.nxrm5cf9f5.212.1 +3 -0
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0362
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- - Rouge1: 75.4021
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- - Rouge2: 60.4781
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- - Rougel: 69.4413
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- - Rougelsum: 69.1531
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  ## Model description
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@@ -52,26 +52,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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- | 1.2638 | 1.0 | 13 | 0.7845 | 46.2506 | 27.5302 | 36.3031 | 36.1488 |
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- | 0.6983 | 2.0 | 26 | 0.6852 | 46.9095 | 28.7666 | 36.3647 | 36.5142 |
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- | 0.6086 | 3.0 | 39 | 0.5691 | 46.4844 | 31.1016 | 40.5842 | 40.5575 |
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- | 0.545 | 4.0 | 52 | 0.5265 | 55.0792 | 41.0166 | 47.3565 | 47.125 |
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- | 0.4797 | 5.0 | 65 | 0.4693 | 60.5996 | 45.0404 | 48.2476 | 48.193 |
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- | 0.4172 | 6.0 | 78 | 0.3998 | 61.1898 | 44.5581 | 51.0268 | 50.9729 |
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- | 0.3694 | 7.0 | 91 | 0.3464 | 65.6304 | 49.6498 | 55.2135 | 55.0835 |
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- | 0.3175 | 8.0 | 104 | 0.2912 | 65.5462 | 49.7933 | 53.8552 | 53.9245 |
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- | 0.2771 | 9.0 | 117 | 0.2090 | 66.7025 | 51.251 | 58.2184 | 57.8748 |
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- | 0.2187 | 10.0 | 130 | 0.1532 | 72.6809 | 58.2315 | 63.6063 | 63.0489 |
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- | 0.1809 | 11.0 | 143 | 0.0823 | 69.1654 | 54.3816 | 58.8206 | 58.877 |
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- | 0.1138 | 12.0 | 156 | 0.0858 | 74.4507 | 58.8272 | 65.7722 | 65.371 |
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- | 0.0813 | 13.0 | 169 | 0.0437 | 76.1358 | 60.4886 | 67.0897 | 66.9479 |
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- | 0.0637 | 14.0 | 182 | 0.0561 | 74.8731 | 61.4541 | 67.0014 | 66.6877 |
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- | 0.0523 | 15.0 | 195 | 0.0698 | 73.008 | 57.968 | 63.4556 | 63.2808 |
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- | 0.0459 | 16.0 | 208 | 0.0556 | 73.1956 | 58.1032 | 65.9982 | 65.5125 |
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- | 0.0352 | 17.0 | 221 | 0.0430 | 75.2864 | 61.8915 | 67.5246 | 67.3903 |
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- | 0.0274 | 18.0 | 234 | 0.0495 | 75.2014 | 59.5957 | 67.6364 | 67.5241 |
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- | 0.0203 | 19.0 | 247 | 0.0390 | 76.9033 | 63.5161 | 72.0725 | 72.1355 |
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- | 0.0233 | 20.0 | 260 | 0.0362 | 75.4021 | 60.4781 | 69.4413 | 69.1531 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0490
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+ - Rouge1: 74.1584
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+ - Rouge2: 57.9788
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+ - Rougel: 62.8448
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+ - Rougelsum: 62.7765
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
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+ | 1.0254 | 1.0 | 35 | 0.7095 | 48.1054 | 30.2028 | 38.8084 | 38.8683 |
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+ | 0.6481 | 2.0 | 70 | 0.6066 | 50.8336 | 34.5183 | 41.7247 | 41.8707 |
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+ | 0.5614 | 3.0 | 105 | 0.5329 | 57.7426 | 37.8419 | 42.9075 | 42.9433 |
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+ | 0.5138 | 4.0 | 140 | 0.4973 | 57.8171 | 36.9019 | 44.6488 | 44.8647 |
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+ | 0.451 | 5.0 | 175 | 0.4401 | 60.916 | 41.087 | 45.4116 | 45.4411 |
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+ | 0.4162 | 6.0 | 210 | 0.3867 | 62.9769 | 43.4407 | 48.2851 | 48.3377 |
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+ | 0.3633 | 7.0 | 245 | 0.3383 | 65.5103 | 47.2327 | 53.4334 | 53.5125 |
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+ | 0.313 | 8.0 | 280 | 0.2834 | 64.057 | 46.1635 | 49.7044 | 49.8158 |
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+ | 0.2669 | 9.0 | 315 | 0.1994 | 66.9064 | 49.5199 | 55.2645 | 55.3093 |
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+ | 0.2073 | 10.0 | 350 | 0.1648 | 69.832 | 52.5513 | 58.7138 | 58.7687 |
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+ | 0.1655 | 11.0 | 385 | 0.1744 | 67.1147 | 49.7127 | 54.8 | 54.8769 |
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+ | 0.1285 | 12.0 | 420 | 0.1074 | 71.6738 | 54.8468 | 60.3888 | 60.5511 |
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+ | 0.0954 | 13.0 | 455 | 0.0886 | 73.8985 | 58.3168 | 64.7526 | 64.7308 |
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+ | 0.0757 | 14.0 | 490 | 0.0684 | 71.6519 | 54.7679 | 60.7249 | 60.8026 |
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+ | 0.0547 | 15.0 | 525 | 0.0679 | 71.475 | 55.8093 | 60.7339 | 60.7417 |
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+ | 0.0426 | 16.0 | 560 | 0.0677 | 73.0471 | 55.8567 | 60.1405 | 60.1719 |
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+ | 0.033 | 17.0 | 595 | 0.0516 | 73.7561 | 57.0512 | 62.7989 | 62.7546 |
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+ | 0.0262 | 18.0 | 630 | 0.0563 | 74.1168 | 58.3878 | 63.5671 | 63.5098 |
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+ | 0.0214 | 19.0 | 665 | 0.0404 | 74.4461 | 58.7092 | 64.3292 | 64.2233 |
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+ | 0.0157 | 20.0 | 700 | 0.0490 | 74.1584 | 57.9788 | 62.8448 | 62.7765 |
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  ### Framework versions
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